Title

An Empirical Study on Large-Scale Content-Based Image Retrieval

Publication Type

Conference Proceeding Article

Publication Date

7-2007

Abstract

One key challenge in content-based image retrieval (CBIR) is to develop a fast solution for indexing high-dimensional image contents, which is crucial to building large-scale CBIR systems. In this paper, we propose a scalable content-based image retrieval scheme using locality-sensitive hashing (LSH), and conduct extensive evaluations on a large image testbed of a half million images. To the best of our knowledge, there is less comprehensive study on large-scale CBIR evaluation with a half million images. Our empirical results show that our proposed solution is able to scale for hundreds of thousands of images, which is promising for building Web-scale CBIR systems.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

IEEE International Conference on Multimedia and Expo, 2007: ICME 2007: 2 - 5 July 2007, Beijing, China: Proceedings

First Page

2206

Last Page

2209

ISBN

9781424410163

Identifier

10.1109/ICME.2007.4285123

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/ICME.2007.4285123